Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 26 for “"Sparse signals"”.
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Nonadaptive lossy encoding of sparse signals
At high rate, a sparse signal is optimally encoded through an adaptive strategy that finds and encodes the signal's representation in the sparsity-inducing basis. This thesis examines how much the distortion rate (D(R)) performance of a nonadaptive encoder, one that is not allowed to explicitly …
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Learning environment simulators from sparse signals
… this work instead seeks to learn from much sparser signals, like the agent's reward. In Chapter 1, we establish a taxonomy of environments and the attributes that make them easier or harder to model through learning. In Chapter 2, we review prior work in the field of environment learning. In …
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Phase Retrieval of Sparse Signals from Magnitude Information
… two approaches are proposed to accomplish sparse signal recovery from fewer magnitude measurements, modified Phase Cut and improved Phase Lift. In these approaches, we combine the phase retrieval methods, both Phase Cut and Phase Lift, which formulate the problem in a higher dimensional …
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Recovery of sparse signals and parameter perturbations from parameterized signal models
Estimating unknown signals from parameterized measurement models is a common problem that arises in diverse areas such as statistics, imaging, machine learning, and signal processing. In many of these problems, however, only a limited amount of data is available to recover the unknown signal. …
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Geometric Conditions for the Recovery of Sparse Signals on Graphs from Measurements Generated with Heat Kernels
… results on signal recovery for graphs, when the signals are functions with small support and what is observed is a noisy version of the signal smoothed by evolving it under the heat equation governed by the graph Laplacian. The results discussed here are in close analogy to the mathematical …
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Approximation of signals and functions in high dimensions with low dimensional structure: finite-valued sparse signals and generalized ridge functions
… A, [54], we assume that we aim to reconstruct a sparse and finite-valued vector. We present an approach that incorporates a finite values prior into basis pursuit, which is one classical reconstruction strategy in compressed sensing. In particular, we address unipolar binary and bipolar ternary …
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Informative sensing : theory and applications
… theory for the sampling and reconstruction of sparse signals. Sparse signals only occupy a tiny fraction of the entire signal space and thus have a small amount of information, relative to their dimension. The theory tells us that the information can be captured faithfully with few random …
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Random observations on random observations: Sparse signal acquisition and processing
… advances in computational power, processing the signals produced in application areas such as imaging, video, remote surveillance, spectroscopy, and genomic data analysis continues to pose a tremendous challenge. Fortunately, in many cases these high-dimensional signals contain relatively little …
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Infrastructure for large-scale tests in marine autonomy
… a recently developed framework for sampling sparse signals that offers dramatic reductions in the number of samples required for high fidelity reconstruction of a field. Our novel CS sampling techniques introduce engineering constraints including movement and measurement costs to better apply …
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Estimation of channelized features in geological media using sparsity constraint
… spatially continuous parameters that exhibit sparseness in an incoherent basis (e.g. a Fourier basis). The solution is constrained to be sparse in the transform domain and the dimension of the search space is effectively reduced to a low frequency subspace to improve estimation efficiency. The …
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Building compressed sensing systems : sensors and analog-to-information converters
… (CS) is a promising method for recovering sparse signals from fewer measurements than ordinarily used in the Shannon's sampling theorem [14]. Introducing the CS theory has sparked interest in designing new hardware architectures which can be potential substitutions for traditional …
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STUDY OF ADAPTIVE COMPRESSIVE SENSING FOR LOW POWER APPLICATIONS
… sensing (CS) technique potentially allows sparse signals to be sampled at rates lower than their Nyquist Rates, making it appealing for implementation of low-power sensors. This dissertation investigates techniques to further improve CS efficiency by adaptively adjusting the sampling rates …
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TOWARDS DATA DRIVEN NETWORK EPIDEMIC MODELING.
… to construct networks of contact from such sparse signals. On the other hand; we present two tractable methodologies for model calibration and optimal control, respectively. These methodologies combine modern machine-learning tools and large-scale mobility datasets with classical tools from …
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Acoustic source localization
… noise is the snapping shrimps. The acoustic signals they emit from snapping their claws hinder technologies, but can also be used as a source of ambient noise illumination due to the rough uniformity in their spatial distribution. Understanding the spatial distributions of these acoustic …
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Efficient and guaranteed algorithms for sparse inverse problems
… and reduced-cost acquisition, by exploiting a sparse signal model. Most notably, recovery of the signal by computationally efficient algorithms is guaranteed for certain randomized acquisition systems. However, there is a discrepancy between the theoretical guarantees and practical …
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NEW ALGORITHMS FOR COMPRESSED SENSING OF MRI: WTWTS, DWTS, WDWTS
… theoretical guarantees on the reconstruction of sparse signals while projection on a low dimensional linear subspace. Further enhancements have extended the CS framework by performing Variable Density Sampling (VDS) or using wavelet domain as sparsity basis generator. Recent work in this approach …
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Compressive sensing based non-destructive testing using ultrasonic arrays.
… compressive sensing approach and the notion of sparse signal recovery to the non-destructive testing application, using ultrasonic arrays. In many signal processing applications including array signal processing, there is a remarkable effort to use the concept of sparsity to solve an …
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Energy-efficient wireless sensors : fewer bits, Moore MEMS
… greater than loX with no loss in fidelity for sparse signals quantized to medium resolutions. We also model the hardware costs for implementing the CS encoder and results from a test chip designed in a 90 nm CMOS process that consumes only 1.9 [mu]W for operating frequencies below 20 kHz, …
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Improving the energy efficiency and reliability of wireless sensor networks using coding techniques
… from communicating information, acquiring the signals of interest can account for a significant fraction of the power consumption of a sensor node. For this reason, the thesis proposes a nonuniform sampling scheme in order to exploit the inherent compressibility and sparse structure of typical …
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Low-rank graphical models and Bayesian inference in the statistical analysis of noisy neural data
… we treat Bayesian methods for the estimation of sparse signals, with application to the locating of synapses in a dendritic tree. We develop a compartmentalized model of the dendritic tree. Building on previous work that applied and generalized ideas of least angle regression to obtain a fast …
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